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Aligning coding sequences with frameshift extension penalties

Overview of attention for article published in Algorithms for Molecular Biology, March 2017
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  • Among the highest-scoring outputs from this source (#39 of 179)
  • Good Attention Score compared to outputs of the same age (67th percentile)

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10 tweeters


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15 Mendeley
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Aligning coding sequences with frameshift extension penalties
Published in
Algorithms for Molecular Biology, March 2017
DOI 10.1186/s13015-017-0101-4
Pubmed ID

Safa Jammali, Esaie Kuitche, Ayoub Rachati, François Bélanger, Michelle Scott, Aïda Ouangraoua


Frameshift translation is an important phenomenon that contributes to the appearance of novel coding DNA sequences (CDS) and functions in gene evolution, by allowing alternative amino acid translations of gene coding regions. Frameshift translations can be identified by aligning two CDS, from a same gene or from homologous genes, while accounting for their codon structure. Two main classes of algorithms have been proposed to solve the problem of aligning CDS, either by amino acid sequence alignment back-translation, or by simultaneously accounting for the nucleotide and amino acid levels. The former does not allow to account for frameshift translations and up to now, the latter exclusively accounts for frameshift translation initiation, not considering the length of the translation disruption caused by a frameshift. We introduce a new scoring scheme with an algorithm for the pairwise alignment of CDS accounting for frameshift translation initiation and length, while simultaneously considering nucleotide and amino acid sequences. The main specificity of the scoring scheme is the introduction of a penalty cost accounting for frameshift extension length to compute an adequate similarity score for a CDS alignment. The second specificity of the model is that the search space of the problem solved is the set of all feasible alignments between two CDS. Previous approaches have considered restricted search space or additional constraints on the decomposition of an alignment into length-3 sub-alignments. The algorithm described in this paper has the same asymptotic time complexity as the classical Needleman-Wunsch algorithm. We compare the method to other CDS alignment methods based on an application to the comparison of pairs of CDS from homologous human, mouse and cow genes of ten mammalian gene families from the Ensembl-Compara database. The results show that our method is particularly robust to parameter changes as compared to existing methods. It also appears to be a good compromise, performing well both in the presence and absence of frameshift translations. An implementation of the method is available at https://github.com/UdeS-CoBIUS/FsePSA.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 33%
Student > Bachelor 3 20%
Student > Master 3 20%
Researcher 2 13%
Professor 1 7%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 27%
Biochemistry, Genetics and Molecular Biology 4 27%
Computer Science 3 20%
Environmental Science 1 7%
Earth and Planetary Sciences 1 7%
Other 1 7%
Unknown 1 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 August 2017.
All research outputs
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Outputs of similar age
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Outputs of similar age from Algorithms for Molecular Biology
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Altmetric has tracked 11,674,646 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 179 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 257,268 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them